Opole University of Technology, Faculty of Electrical Engineering, Automatic Control and Informatics, 45-758 Opole, Poland.
University of Greenwich, Department of Computing and Information Systems, SE10 9LS London, UK.
Sensors (Basel). 2020 Feb 2;20(3):807. doi: 10.3390/s20030807.
This paper covers a brief review of both the advantages and disadvantages of the implementation of various smoothing filters in the analysis of electroencephalography (EEG) data for the purpose of potential medical diagnostics. The EEG data are very prone to the occurrence of various internal and external artifacts and signal distortions. In this paper, three types of smoothing filters were compared: smooth filter, median filter and Savitzky-Golay filter. The authors of this paper compared those filters and proved their usefulness, as they made the analyzed data more legible for diagnostic purposes. The obtained results were promising, however, the studies on finding perfect filtering methods are still in progress.
本文简要回顾了在进行潜在医学诊断时，在分析脑电图 (EEG) 数据中实施各种平滑滤波器的优缺点。EEG 数据非常容易受到各种内部和外部伪影以及信号失真的影响。在本文中，比较了三种平滑滤波器：平滑滤波器、中值滤波器和 Savitzky-Golay 滤波器。本文作者对这些滤波器进行了比较，并证明了它们的有用性，因为它们使分析后的数据更便于诊断。得到的结果是有希望的，但是，寻找完美的滤波方法的研究仍在进行中。
